1. Field of the Invention
The present invention is directed to correlating sensor detections and, more particularly, to multitarget tracking systems for correlating data from radar and similar target detection systems.
2. Description of the Related Art
In any multitarget tracking system, the sensor detections, i.e., the reports produced by the sensors, must be associated into labeled collections of the same physical object before a track can be established on that object. The required association or correlation is difficult because the sensor systems generally cannot provide reliable target identification to be used as an association discriminant. In other words only if a sensor system provides a unique identification tag on each report, can target tracks be established easily, by associating all reports having matching identification tags. An example of such a sensor system is the Identification Friend or Foe (IFF) system. Since most multitarget tracking systems do not have this capability, it is necessary to detect target tracks based only on the proximity of sensor reports from the same or nearly same physical location over a period of time. This is not easily accomplished when there are a large number of targets (high target density), low sensor data rates, missed detections, false alarms, inaccurate sensor measurements or multiple sensor types which measure dissimilar parameters.
Typical track initiation techniques attempt to form tentative target tracks by associating all possible combinations of sensor reports from scan to scan. The main problem with this technique is that, in dense environments where the number of targets is unknown, too many tentative tracks have plausible behavior over long periods of time; thus significantly increasing the false track rate, processor workload, memory requirements and response time. Because these conventional techniques attempt to "explain" each and every sensor report, the occurrence of sensor false alarms can lead to the generation of false target tracks or the corruption of existing target tracks, further degrading system performance.
Conventional solutions to the problems identified above include the use of several types of consistency checks to detect tracks that exhibit inconsistent platform dynamics. These tracks are then either deleted, split into multiple tracks, or merged with other tracks into a cluster. The goal in these systems is to explain everything while minimizing the false track rate by converging to some optimum number of target (or cluster) tracks over a period of time, a goal which is not often achieved in practice. In addition, these solutions add to the processing overhead and thus must be both efficient and effective to have a net positive affect in addressing the problems identified above.